_base_ = 'mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
# We also need to change the num_classes in head to match the dataset's annotation


# Modify dataset related settings

classes = ("layer1", "layer2", "layer3", "layer4")


data_root = '/home/zhb/Desktop/SZQ/dataset/'
data = dict(
    samples_per_gpu=12,
    train=dict(
        img_prefix=data_root + 'test/',
        classes=classes,
        ann_file=data_root + 'test_a.json'),
    val=dict(
        img_prefix=data_root + 'valid/',
        classes=classes,
        ann_file=data_root + 'valid_a.json'),
    test=dict(
        img_prefix=data_root + 'test/',
        classes=classes,
        ann_file=data_root + 'test_a.json')
)
evaluation = dict(metric=['bbox'])
